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fix hybrid searching

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LoiTra 2025-07-28 17:09:37 +07:00 committed by loitragg
parent 3210b2fa45
commit 85775a772c
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4 changed files with 369 additions and 19 deletions
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3
Dockerfile
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@ -94,6 +94,9 @@ ENV TIKTOKEN_ENCODING_NAME="cl100k_base" \
## Hugging Face download cache ##
ENV HF_HOME="/app/backend/data/cache/embedding/models"
## FastEmbed cache directory ##
ENV FASTEMBED_CACHE_PATH="/app/backend/data/cache/fastembed"
## Torch Extensions ##
# ENV TORCH_EXTENSIONS_DIR="/.cache/torch_extensions"

101
backend/open_webui/retrieval/utils.py
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@ -243,6 +243,103 @@ async def query_doc_with_hybrid_search(
log.warning(f"query_doc_with_hybrid_search:no_docs {collection_name}")
return {"documents": [], "metadatas": [], "distances": []}
# Use Qdrant's integrated search
if VECTOR_DB == "qdrant":
log.info("Using Qdrant search (hybrid if enabled)")
# Generate query embedding
query_embedding = embedding_function(query, RAG_EMBEDDING_QUERY_PREFIX)
# Use Qdrant's search method with query_text
result = VECTOR_DB_CLIENT.search(
collection_name=collection_name,
vectors=[query_embedding],
limit=k_reranker, # Get more results for reranking
query_text=query, # type: ignore # Enables hybrid search internally
)
if result and result.documents and result.documents[0]:
# Convert SearchResult to the expected format
documents = result.documents[0]
metadatas = result.metadatas[0] if result.metadatas else [{}] * len(documents)
distances = result.distances[0] if result.distances else [0.0] * len(documents)
# Apply reranking if available
if reranking_function and len(documents) > 1:
log.debug("Applying reranking to Qdrant hybrid search results")
# Create documents for reranking
docs_for_reranking = [
Document(page_content=doc, metadata=meta)
for doc, meta in zip(documents, metadatas)
]
# Apply reranking - call with correct signature (single argument: list of query-doc pairs)
query_doc_pairs = [(query, doc.page_content) for doc in docs_for_reranking]
scores = reranking_function(query_doc_pairs)
# Process scores into ranked documents (similar to RerankCompressor)
docs_with_scores = list(zip(docs_for_reranking, scores.tolist() if not isinstance(scores, list) else scores))
# Filter by relevance threshold
if r > 0.0:
docs_with_scores = [(d, s) for d, s in docs_with_scores if s >= r]
# Sort by score (highest first) and limit to k
docs_with_scores = sorted(docs_with_scores, key=lambda x: x[1], reverse=True)[:k]
if docs_with_scores:
# Extract final results
final_documents = [doc.page_content for doc, score in docs_with_scores]
final_metadatas = []
final_distances = []
for doc, score in docs_with_scores:
metadata = doc.metadata.copy()
metadata["score"] = score
final_metadatas.append(metadata)
final_distances.append(score)
result_dict = {
"distances": [final_distances],
"documents": [final_documents],
"metadatas": [final_metadatas],
}
else:
# No documents passed relevance threshold, return empty result
result_dict = {
"distances": [[]],
"documents": [[]],
"metadatas": [[]],
}
else:
# No reranking, just apply relevance threshold and limit
if r > 0.0:
# Filter by relevance threshold
filtered_indices = [i for i, score in enumerate(distances) if score >= r][:k]
filtered_documents = [documents[i] for i in filtered_indices]
filtered_metadatas = [metadatas[i] for i in filtered_indices]
filtered_distances = [distances[i] for i in filtered_indices]
else:
# No threshold, just limit to k
filtered_documents = documents[:k]
filtered_metadatas = metadatas[:k]
filtered_distances = distances[:k]
result_dict = {
"distances": [filtered_distances],
"documents": [filtered_documents],
"metadatas": [filtered_metadatas],
}
log.info(
f"query_doc_with_hybrid_search:qdrant_native_result {len(result_dict['documents'][0])} documents"
)
return result_dict
else:
log.warning("Qdrant hybrid search returned no results, falling back to LangChain approach")
log.debug(f"query_doc_with_hybrid_search:doc {collection_name}")
bm25_texts = (
@ -313,7 +410,7 @@ async def query_doc_with_hybrid_search(
}
log.info(
"query_doc_with_hybrid_search:result "
"query_doc_with_hybrid_search:langchain_result "
+ f'{result["metadatas"]} {result["distances"]}'
)
return result
@ -1290,6 +1387,8 @@ class RerankCompressor(BaseDocumentCompressor):
scores = None
if reranking:
scores = self.reranking_function(query, documents)
# query_doc_pairs = [(query, doc.page_content) for doc in documents]
# scores = self.reranking_function(query_doc_pairs)
else:
if not SENTENCE_TRANSFORMERS_AVAILABLE:
raise ImportError("sentence_transformers is not available. Please install it to use reranking functionality.")

283
backend/open_webui/retrieval/vector/dbs/qdrant_multitenancy.py
View file

@ -2,7 +2,6 @@ import logging
from typing import Optional, Tuple, List, Dict, Any
from urllib.parse import urlparse
import grpc
from open_webui.config import (
QDRANT_API_KEY,
QDRANT_GRPC_PORT,
@ -12,6 +11,7 @@ from open_webui.config import (
QDRANT_COLLECTION_PREFIX,
QDRANT_TIMEOUT,
QDRANT_HNSW_M,
ENABLE_RAG_HYBRID_SEARCH,
)
from open_webui.env import SRC_LOG_LEVELS
from open_webui.retrieval.vector.main import (
@ -21,7 +21,6 @@ from open_webui.retrieval.vector.main import (
VectorItem,
)
from qdrant_client import QdrantClient as Qclient
from qdrant_client.http.exceptions import UnexpectedResponse
from qdrant_client.http.models import PointStruct
from qdrant_client.models import models
@ -141,23 +140,33 @@ class QdrantClient(VectorDBBase):
):
"""
Creates a collection with multi-tenancy configuration and payload indexes for tenant_id and metadata fields.
Also creates sparse vector configuration for hybrid search support.
"""
# Create collection with both dense and sparse vector support
self.client.create_collection(
collection_name=mt_collection_name,
vectors_config=models.VectorParams(
size=dimension,
distance=models.Distance.COSINE,
on_disk=self.QDRANT_ON_DISK,
),
vectors_config={
"dense": models.VectorParams(
size=dimension,
distance=models.Distance.COSINE,
on_disk=self.QDRANT_ON_DISK,
)
},
# Disable global index building due to multitenancy
# For more details https://qdrant.tech/documentation/guides/multiple-partitions/#calibrate-performance
hnsw_config=models.HnswConfigDiff(
payload_m=self.QDRANT_HNSW_M,
m=0,
),
# Add sparse vectors configuration for BM25-like hybrid search
sparse_vectors_config={
"bm25": models.SparseVectorParams(
modifier=models.Modifier.IDF,
)
},
)
log.info(
f"Multi-tenant collection {mt_collection_name} created with dimension {dimension}!"
f"Multi-tenant collection {mt_collection_name} created with dimension {dimension} and sparse vector support!"
)
self.client.create_payload_index(
@ -208,6 +217,190 @@ class QdrantClient(VectorDBBase):
if not self.client.collection_exists(collection_name=mt_collection_name):
self._create_multi_tenant_collection(mt_collection_name, dimension)
def _hybrid_search(
self,
collection_name: str,
query_vector: List[float],
query_text: str,
limit: int,
) -> Optional[SearchResult]:
"""
Perform Qdrant native hybrid search using prefetch + RRF fusion.
This method uses Qdrant's named vectors (dense + sparse) with prefetch queries
and applies RRF (Reciprocal Rank Fusion) for optimal result combination.
Args:
collection_name: Name of the collection to search
query_vector: Dense vector representation of the query
query_text: Text query for sparse vector generation
limit: Maximum number of results to return
Returns:
SearchResult with RRF-fused hybrid results
"""
if not self.client or not query_vector:
return None
mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
if not self.client.collection_exists(collection_name=mt_collection):
log.debug(f"Collection {mt_collection} doesn't exist, hybrid search returns None")
return None
tenant_filter = _tenant_filter(tenant_id)
try:
# Use Qdrant's native hybrid search with prefetch + RRF fusion (like user's example)
# Generate sparse vector from query text using FastEmbed or fallback
sparse_vector = self._query_to_sparse_vector(query_text)
# Create prefetch queries for both dense and sparse vectors
prefetch_queries = [
# Dense vector prefetch
models.Prefetch(
query=query_vector,
using="dense",
limit=limit * 2, # Get more candidates for better fusion
filter=models.Filter(must=[tenant_filter]),
)
]
# Add sparse vector prefetch if we have terms
if sparse_vector["indices"]:
prefetch_queries.append(
models.Prefetch(
query=models.SparseVector(
indices=sparse_vector["indices"],
values=sparse_vector["values"],
),
using="bm25",
limit=limit * 2,
filter=models.Filter(must=[tenant_filter]),
)
)
# Use Qdrant's native fusion - RRF is currently the most robust option
# For custom weighting, Qdrant supports score formulas (see alternative implementation below)
query_response = self.client.query_points(
collection_name=mt_collection,
prefetch=prefetch_queries,
query=models.FusionQuery(
fusion=models.Fusion.RRF,
),
limit=limit,
with_payload=True,
)
get_result = self._result_to_get_result(query_response.points)
return SearchResult(
ids=get_result.ids,
documents=get_result.documents,
metadatas=get_result.metadatas,
distances=[[(point.score + 1.0) / 2.0 for point in query_response.points]],
)
except Exception as e:
log.warning(f"Qdrant native hybrid search failed, trying fallback: {e}")
# Fallback to client-side hybrid scoring if native approach fails
try:
candidates_limit = max(limit * 3, 100)
dense_results = self.client.query_points(
collection_name=mt_collection,
query=query_vector,
using="dense", # Use named dense vector
limit=candidates_limit,
query_filter=models.Filter(must=[tenant_filter]),
)
# Apply simple score normalization for fallback
get_result = self._result_to_get_result(dense_results.points)
return SearchResult(
ids=get_result.ids,
documents=get_result.documents,
metadatas=get_result.metadatas,
distances=[[(point.score + 1.0) / 2.0 for point in dense_results.points]],
)
except Exception as e2:
log.warning(f"Fallback hybrid search failed: {e2}")
# Final fallback to regular dense search
return self.search(collection_name, [query_vector], limit)
def _get_bm25_embedding_model(self):
"""
Get or create the BM25 embedding model using FastEmbed.
"""
if not hasattr(self, '_bm25_model') or self._bm25_model is None:
try:
from fastembed import SparseTextEmbedding # type: ignore
self._bm25_model = SparseTextEmbedding("Qdrant/bm25")
log.info("Initialized FastEmbed BM25 sparse embedding model")
except ImportError:
log.warning("FastEmbed not available, will use fallback sparse vector generation")
self._bm25_model = None
except Exception as e:
log.warning(f"Failed to initialize FastEmbed BM25 model: {e}")
self._bm25_model = None
return self._bm25_model
def _text_to_sparse_vector(self, text: str) -> Dict[str, List]:
"""
Convert text to sparse vector representation using FastEmbed's BM25 model.
Falls back to simple implementation if FastEmbed is not available.
"""
if not text:
return {"indices": [], "values": []}
# Try to use FastEmbed's BM25 model first
bm25_model = self._get_bm25_embedding_model()
if bm25_model is not None:
try:
# Use FastEmbed to generate proper BM25 sparse embedding
sparse_embeddings = list(bm25_model.passage_embed([text]))
if sparse_embeddings and len(sparse_embeddings) > 0:
sparse_embedding = sparse_embeddings[0]
# Convert to the format expected by Qdrant
return {
"indices": sparse_embedding.indices.tolist(),
"values": sparse_embedding.values.tolist()
}
except Exception as e:
log.warning(f"FastEmbed BM25 embedding failed: {e}, using fallback")
# No FastEmbed available, return empty sparse vector
return {"indices": [], "values": []}
def _query_to_sparse_vector(self, query_text: str) -> Dict[str, List]:
"""
Convert query text to sparse vector representation using FastEmbed's BM25 model.
Uses query_embed for better query representation vs passage_embed.
"""
if not query_text:
return {"indices": [], "values": []}
# Try to use FastEmbed's BM25 model for query embedding
bm25_model = self._get_bm25_embedding_model()
if bm25_model is not None:
try:
# Use query_embed for queries (optimized for query representation)
sparse_embeddings = list(bm25_model.query_embed([query_text]))
if sparse_embeddings and len(sparse_embeddings) > 0:
sparse_embedding = sparse_embeddings[0]
# Convert to the format expected by Qdrant
return {
"indices": sparse_embedding.indices.tolist(),
"values": sparse_embedding.values.tolist()
}
except Exception as e:
log.warning(f"FastEmbed BM25 query embedding failed: {e}, using fallback")
# No FastEmbed available, return empty sparse vector
return {"indices": [], "values": []}
def has_collection(self, collection_name: str) -> bool:
"""
Check if a logical collection exists by checking for any points with the tenant ID.
@ -256,25 +449,49 @@ class QdrantClient(VectorDBBase):
)
def search(
self, collection_name: str, vectors: List[List[float | int]], limit: int
self, collection_name: str, vectors: List[List[float | int]], limit: int, query_text: Optional[str] = None
) -> Optional[SearchResult]:
"""
Search for the nearest neighbor items based on the vectors with tenant isolation.
Uses hybrid search when ENABLE_RAG_HYBRID_SEARCH is True and query_text is provided.
"""
if not self.client or not vectors:
return None
# Use hybrid search if enabled and query text is available
if ENABLE_RAG_HYBRID_SEARCH and query_text:
return self._hybrid_search(
collection_name=collection_name,
query_vector=vectors[0],
query_text=query_text,
limit=limit
)
# Fallback to regular dense vector search
mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
if not self.client.collection_exists(collection_name=mt_collection):
log.debug(f"Collection {mt_collection} doesn't exist, search returns None")
return None
tenant_filter = _tenant_filter(tenant_id)
query_response = self.client.query_points(
collection_name=mt_collection,
query=vectors[0],
limit=limit,
query_filter=models.Filter(must=[tenant_filter]),
)
try:
# Try to use named dense vector first (for hybrid-enabled collections)
query_response = self.client.query_points(
collection_name=mt_collection,
query=vectors[0],
using="dense",
limit=limit,
query_filter=models.Filter(must=[tenant_filter]),
)
except Exception:
# Fallback for collections without named vectors (legacy collections)
query_response = self.client.query_points(
collection_name=mt_collection,
query=vectors[0],
limit=limit,
query_filter=models.Filter(must=[tenant_filter]),
)
get_result = self._result_to_get_result(query_response.points)
return SearchResult(
ids=get_result.ids,
@ -327,14 +544,44 @@ class QdrantClient(VectorDBBase):
def upsert(self, collection_name: str, items: List[VectorItem]):
"""
Upsert items with tenant ID.
Upsert items with tenant ID, dense vectors, and sparse vectors for hybrid search.
"""
if not self.client or not items:
return None
mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
dimension = len(items[0]["vector"])
dimension = len(items[0]["vector"]) # type: ignore # Items are dicts, not VectorItem instances
self._ensure_collection(mt_collection, dimension)
points = self._create_points(items, tenant_id)
# Create points with both dense and sparse vectors
points = []
for item in items:
# Generate sparse vector from text content for BM25-like search
sparse_vector = self._text_to_sparse_vector(item["text"]) # type: ignore
# Create vector dict with named vectors (similar to user's example)
vector_dict = {
"dense": item["vector"], # type: ignore # Dense semantic vector
}
# Add sparse vector if we have terms
if sparse_vector["indices"]:
vector_dict["bm25"] = models.SparseVector( # type: ignore
indices=sparse_vector["indices"],
values=sparse_vector["values"],
)
points.append(
PointStruct(
id=item["id"], # type: ignore
vector=vector_dict, # type: ignore # Qdrant client accepts dict for named vectors
payload={
"text": item["text"], # type: ignore
"metadata": item["metadata"], # type: ignore
TENANT_ID_FIELD: tenant_id,
},
)
)
self.client.upload_points(mt_collection, points)
return None

1
backend/requirements.txt
View file

@ -54,6 +54,7 @@ opensearch-py==2.8.0
transformers==4.57.3
sentence-transformers==5.1.2
fastembed==0.7.1
accelerate
pyarrow==20.0.0 # fix: pin pyarrow version to 20 for rpi compatibility #15897
einops==0.8.1